Technical Documentation on Data Sources and Methods for Statistical Performance Indicators

Introduction

In this program, I will clean and reshape the raw data files in the 01_rawdata folder in order to produce the Statistical Performance Indicators.

Below is an overview of the SPI framework.

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Key Discussion Points

Here are a few concerns that I have at the moment, but this is by no means an exhaustive list. Overall, there are some indicators with issues in terms of coverage across countries. There are also differences in the length of the time series for each indicator. Here is a detailed breakdown by indicator.

1. Dimension 1 needs more work adding source documents, NSO social media accounts. It also needs work refining scoring methodology. This should be discussed as a team.
   
2. Dimension 2 has no information for the Advisory/Analytical Services dimension.
3. Dimension 3 is based on the 15 AKI indicators we defined for the original SPI. This needs to be discussed, as John had proposed going to NSO sites and collecting SDG indicator information. I abandoned this approach, because there is no straightforward way of scraping this info. To discuss as a team.
   
4. Dimension 4 needs more work and refinement particularly on the Private/Citizen generated data section.
   
5. Dimension 5 also needs work with having a complete set of data for all countries. There are major coverage gaps for all indicators, except for the Standards dimension which comes from the SPI team.

Data Use

Cleaning for Data Use Indicators. Data Use (5 Indicators):
- 1.1_DUNL - Indicator 1.1: Data use by national legislature
- 1.2_DUNE - Indicator 1.2: Data use by national executive branch
- 1.3_DUCS - Indicator 1.3: Data use by civil society
- 1.4_DUAC - Indicator 1.4: Data use by academia
- 1.5_DUIO - Indicator 1.5: Data use by international organizations

Indicator 1.1: Data use by national legislature

Based on PARIS21 data use indicator (Chapter 4 of Statistical Capacity Development Outlook) using national legislature website as source.

Indicator 1.2: Data use by national executive branch

Based on PARIS21 data use indicator (Chapter 4 of Statistical Capacity Development Outlook) using national development plans and poverty reduction plans as a source.

Our main source for this is the FAOLEX database of legislation and policy documents maintained by the FAO.

Indicator 1.3: Data use by civil society

Based on PARIS21 data use indicator (Chapter 4 of Statistical Capacity Development Outlook) using main social media platform in use in country as source.

We are pulling data from Twitter at this point. We have only accessed a handful of accounts from NSOs, but this could be quickly scaled up.

Indicator 1.4: Data use by academia

The idea for this indicator is that countries should be producing statistical products that are utilized, in this case by academia. As a measure of this, we use the IHSN database on censuses and surveys conducted by counties and calculate a ranking of countries based on the total number of downloads of the censuses and surveys produced by that country on a per capita basis. In other words, we calculate the total number of downloads of censuses and surveys for a country, divided by the population of that country, to produce a ranking. Countries with low scores on this metric, may either be producing censuses and surveys that are not in high demand or are not producing or adding a sufficient number of censuses and surveys to IHSN to register highly on this measure.

The source for this indicator is the IHSN.

The mission of the IHSN is to improve the availability, accessibility, and quality of survey data within developing countries, and to encourage the analysis and use of this data by national and international development decision makers, the research community, and other stakeholders.

To support this mission, the key objectives of the IHSN are:

Improved coordination of internationally sponsored survey programs, with emphasis on timing, sequencing, frequency, and cost-effectiveness Availability of coordinated and practical technical and methodological guidelines for all stages of the survey life cycle Availability of a central survey data catalog which would inform data users of the availability of survey and census data from multiple sources Availability of standards, tools, and guidelines that would allow data producers to document, disseminate, and preserve microdata according to international standards and best practices Improved collaboration between data producers and users

IHSN has a database of more than 7,483 surveys as of August 5, 2020.

Indicator 1.5: Data use by international organizations

New indicator of level of congruence between UNSD and national database of SDG indicators. For each of the indicators calculate whether the number in the database for the most recent year is the same or different. The indicator is the percentage that are the same.

Note: > I met with significant technical challenges trying to compile this information. It would be a useful data collection exercise, but will require significant time and effort. Because this has not yet taken place, I have not produced indicators for this topic area.

An alternative would be to form this indicator using some of the quality metadata we have collected for the Availability of Key Indicators. This could include the poverty indicators, child mortality, maternal mortality, safely managed drinking water, electricity, and debt.

Data Services

Cleaning for Data Services Indicators. Data Services (4 Indicators):
- 2.1_DSDR - Indicator 2.1: Data releases
- 2.2_DSOA - Indicator 2.2: Online access
- 2.3_DSAS - Indicator 2.3: Advisory/ Analytical Services
- 2.4_DSDS - Indicator 2.4: Data services

Indicator 2.1: Data Releases

Data Dissemination Standard (SDDS) and electronic General Data Dissemination Standard (e-GDDS) were established by the International Monetary Fund (IMF) for member countries that have or that might seek access to international capital markets, to guide them in providing their economic and financial data to the public. Although subscription is voluntary, the subscribing member needs to be committed to observing the standard and provide information about its data and data dissemination practices (metadata). The metadata are posted on the IMF’s SDDS and e-GDDS websites.

1 Point. Subscribing to IMF SDDS+ or SDDS standards 0.5 Points. Subscribing to IMF e-GDDS standards 0 Points. Otherwise

Indicator 2.2: Online access

This indicator measures the richness and openness of online access.

Source

Our source for this indicator is Open Data Watch. From Open Data watch:

The Open Data Inventory (ODIN) assesses the coverage and openness of official statistics to help identify gaps, promote open data policies, improve access, and encourage dialogue between national statistical offices (NSOs) and data users. ODIN 2018/19 includes 178 countries, including most all OECD countries. Two-year comparisons are for all countries with two years of data between 2015-2017. Scores can be compared across topics and countries.

We use the Openness score from ODIN for this measure. The score ranges from 0-100. It contains scores along five dimensions:
- Machine Readability
- Non-Proprietary format
- Download Options
- Metadata Available
- Terms of Use

A description for each of these five dimensions is below:

Machine Readability

Openness element 1 measures whether data are available in a machine readable format such as XLS, XLSX, CSV, and JSON. Machine-readable file formats allow users to easily process data using a computer. When data are made available in formats that are not machine readable, users cannot easily access and modify the data, which severely restricts the scope of the data’s use. In many cases PDF versions of datasets within reports can be useful to users, as the text in conjunction with the tables gives context and explanation to the figures which helps less technical users understand the data. Because of this, ODIN assessments do not penalize countries for making datasets available in PDF or other non-machine readable formats, unless these formats are the only option for exporting data. Scores are not penalized for having identical datasets in both machine readable and non-readable formats. Compression formats do not affect machine readability scores, only non-proprietary scores (see next page). Scores are given by data category, not indicator.

Non-Proprietary format

For the elements of data openness, scoring is calculated independent of the data coverage. If data files are compressed in RAR format (which is proprietary), data for that indicator should be considered proprietary even if the enclosing files are in a non-proprietary format. Files compressed in ZIP format are not affected.

Download Options

Openness element 3 measures whether data are available with three different download options: bulk download, API, and user-select options. A bulk download is defined at the indicator level as: The ability to download all data recorded in ODIN for a particular indicator (all years, disaggregations, and subnational data) in one file, or multiple files that can be downloaded simultaneously. Bulk downloads are a key component of the Open Definition, which requires data to be “provided as a whole . . . and downloadable via the internet.” User-selectable download options are defined as: Users must be able to select an indicator and at least one other dimension to create a download or table. These dimensions could include time periods, geographic disaggregations, or other recommended disaggregations. An option to choose the file export format is not enough. API stands for Application Programming Interface. Ideally, APIs should be clearly displayed on the website. ODIN assumes APIs are available for the NSOs entire data collection used in ODIN, unless clearly stated. ODIN assessors do not register for use or test API functionality. For more information on APIs, see this guide. Scores are given by data category, not indicator.

Metadata Available

Openness element 4 measures whether metadata are made available. Scores are given by data category, not indicator. Metadata are defined at the indicator level as information about how the data are defined/calculated and collected. ODIN classifies metadata into three categories: (1) Not Available, (2) Incomplete, and (3) Complete. The following must be available to classify metadata as complete: • Definition of the indicator, or definition of key terms used in the indicator description (as applicable), or how the indicator was calculated. • Publication (date of upload), compilation date (date on front of report is not sufficient), or date dataset was last updated. • Name of data source (what agency collected the data). If the metadata only have one or two of the above elements, they are scored as incomplete

Terms of Use

Openness element 5 measures whether data are available with an open terms of use. Generally, terms of use (TOU) will apply to an entire website or data portal (unless otherwise specified). In these cases, all data found on the same website and/or portal will receive the same score. If a portal is located on the same domain as the NSO website, the terms of use on the NSO site will apply. If the data are located on a portal or website on a different domain, another terms of use will need to be present. For a policy/ license to be accepted as a terms of use, it must clearly refer to the data found on the website. Terms of use that refer to nondata content (such as pictures, logos, etc.) of the website are not considered. A copyright symbol at the bottom of the page is not sufficient. A sentence indicating a recommended citation format is not sufficient. Terms of use are classified the following ways: (1) Not Available, (2) Restrictive, (3) Semi-Restrictive, and (4) Open. If the TOU contains one or more restrictive clauses, it receives 0 points and is classified as “restrictive.” Restrictive clauses include:

For more details, consult the ODIN technical documentation: https://docs.google.com/document/d/1ubPL1l_3im9bjlCVZ6W2ICAy6UAiXl1hGeA1aXImkxI/edit#

Indicator 2.3: Advisory/ Analytical Services

No established source exists for this indicator. This is experimental.

Indicator 2.4: Data services

NSO has a listing of surveys and microdata sets that can provide the necessary data and reference for follow-up. Upon well-defined request and procedure per the national law and practice, users and practitioners can obtain the data collected from the households and businesses when needed.

NADA is an open source microdata cataloging system, compliant with the Data Documentation Initiative (DDI) and Dublin Core’s RDF metadata standards. It serves as a portal for researchers to browse, search, compare, apply for access, and download relevant census or survey datasets, questionnaires, reports and other information.

1 Point. Yes 0 Points. No

Data Products

Cleaning for Data Products Indicators. Data Products (4 Indicators):
- 3.1_DPSS - Indicator 3.1: social statistics
- 3.2_DPES - Indicator 3.2: economic statistics
- 3.3_DPEN - Indicator 3.3 environmental statistics
- 3.4_DPIS - Indicator 3.4: institutional statistics

Below is the list of AKI indicators:

Indicator 3.1: social statistics

• AKI 3.1: Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)
  
• AKI 3.2: Food Insecurity Experience Scale
• AKI 3.3: Mortality rate, under-5 (per 1,000 live births)
  
• AKI 3.4: Proportion of children and young people in grades 2 or 3 achieving at least a minimum proficiency level in reading and mathematics, by sex.
  
• AKI 3.5: Maternal Mortality
• AKI 3.6: People using safely managed drinking water services (% of population)

Indicator 3.2: economic statistics

• AKI 3.7: Access to electricity (% of population)
  
• AKI 3.8: Unemployment, total (% of total labor force)
  
• AKI 3.9: Manufacturing, value added (% of GDP)
  
• AKI 3.10: Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population (%)
  
• AKI 3.14: Quarterly GDP

Indicator 3.3 environmental statistics

• AKI 3.11: Level of water stress: freshwater withdrawal as a proportion of available freshwater resources
  
• AKI 3.12: Renewable energy consumption (% of total final energy consumption)
  
• AKI 3.13: Households and NPISHs Final consumption expenditure (current LCU)

Indicator 3.4: institutional statistics

• AKI 3.15: Debt service (PPG and IMF only, % of exports of goods, services and primary income)

AKI 3.1, 3.9, 3.10, 3.11, 3.12, 3.13

First we will pull data for indicators coming straight from WDI. For some indicators, we will use alternative sources.

Indicators coming from the WDI directly are:

• AKI 3.1: Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)
  
• AKI 3.9: Manufacturing, value added (% of GDP)
  
• AKI 3.10: Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population (%)
  
• AKI 3.11: Level of water stress: freshwater withdrawal as a proportion of available freshwater resources
  
• AKI 3.12: Renewable energy consumption (% of total final energy consumption)
  
• AKI 3.13: Households and NPISHs Final consumption expenditure (current LCU)

Scoring:

1 Point. 3 or more values available within past 5 years 0.6 Points. 2 values available within past 5 years;
0.3 Points. 1 values available within past 5 years;
0 Points. None within past 5 years

##########
# Pull Tags for WDI Data
##########

# make request to World Bank API
wdiRequest <- GET(url = "http://api.worldbank.org/v2/indicator?per_page=20000&format=json&source=2")
wdiResponse <- content(wdiRequest, as = "text", encoding = "UTF-8")

# Parse the JSON content and convert it to a data frame.
wdisJSON <- jsonlite::fromJSON(wdiResponse, flatten = TRUE) %>%
    data.frame()

EdStatsRequest <- GET(url = "http://api.worldbank.org/v2/indicator?per_page=20000&format=json&source=12")
EdStatsResponse <- content(EdStatsRequest, as = "text", encoding = "UTF-8")

# Parse the JSON content and convert it to a data frame.
EdStatsJSON <- jsonlite::fromJSON(EdStatsResponse, flatten = TRUE) %>%
  data.frame()

aki<- c(
        'Pupils below minimum reading proficiency at end of primary (%). Low GAML threshold',
        'People using safely managed drinking water services (% of population)',
        'Unemployment, total (% of total labor force) (national estimate)' ,
        'Manufacturing, value added (% of GDP)',
        'Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population (%)',
        'Level of water stress: freshwater withdrawal as a proportion of available freshwater resources',
        'Renewable energy consumption (% of total final energy consumption)',
        'Households and NPISHs Final consumption expenditure (current LCU)',
        'Debt service (PPG and IMF only, % of exports of goods, services and primary income)'  )

get_tag_aki_df<-wdisJSON %>%
  bind_rows(EdStatsJSON) %>%
  filter((name %in% aki )) %>%
  arrange(factor(name, levels = aki)) %>%
  select(id, name,  sourceOrganization) 

#get WDI metadata infor
cache_list<-wbstats::wbcache()
country_list <- wbstats::wbcountries()

aki_list<-get_tag_aki_df[,'id']

for (reference_year in 2016:2019) {
  temp <-wbstats::wb(country="countries_only", 
              indicator=aki_list,
              startdate=reference_year-5,
              enddate=reference_year,
              return_wide = T,
              removeNA=FALSE) %>%
          filter(((reference_year-as.numeric(date))<=5) & (reference_year>=as.numeric(date))) %>% #filter out years outside reference window of 3 years     
          write_excel_csv(path = paste(output_dir, "/D3.AKI_data_pull_",reference_year,".csv", sep="" )) %>%
          mutate_at(.vars=aki_list, ~if_else(is.na(.),0,1)) %>% #create 0,1 variable for whether data point exists for country
          group_by(iso3c, country) %>%
          summarise_all((~(if(is.numeric(.)) sum(., na.rm = TRUE) else first(.)))) %>% #group by country to create one observation per country                 containing whether or not data point existed
          mutate_at(.vars=aki_list, ~case_when(
            .>=3 ~ 1,
            .==2 ~ 0.6,
            .==1 ~ 0.3,
            .==0 ~ 0, 
            TRUE ~ 0
          )) %>% # 1 point for at least 3 values, 0.6 for 2 values, 0.3 for 1 values, 0 otherwise
          mutate(date=reference_year) %>%
          ungroup() %>%
          select(iso3c, country, date,  aki_list) %>%
          rename_at(aki_list, ~(paste('SPI.D3.',.,sep=""))) %>% #add 'D3.' as prefix before these indicators.
          left_join(country_list) #attach country metadata


  assign(paste("D3.AKI",reference_year,sep="_"), temp)
}

AKI 3.1: Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)

The data will be pulled from the WDI and combined with metadata from the Povcalnet

Scoring is as follows: Quality (0.5 points total): 0.5 Point. Comparable data lasting at least two years within past 5 years
0 Points. No comparable data within past 5 years

Frequency (0.5 points total): 0.5 Point. 3 or more values available within past 5 years
0.3 Points. 2 values available within past 5 years;
0.15 Points. 1 values available within past 5 years;
0 Points. None within past 5 years

AKI 3.2: Hunger

We examine the Food Insecurity Experience Scale from the FAO (http://www.fao.org/faostat/en/#data/FS). Data for food insecurity was pulled on May 1, 2020.

Scoring 1 Point. 3 or more values available within past 5 years 0.6 Points. 2 values available within past 5 years;
0.3 Points. 1 values available within past 5 years;
0 Points. None within past 5 years

AKI 3.3: Mortality rate, under-5 (per 1,000 live births)

The data in WDI is modeled, based on HHS and Vital Registration. We use the following as a source for raw data produced by national statistical offices that is used in this modeling.

https://childmortality.org/data

Data was pulled on April 13, 2020.

Countries are ranked by source quality (admin data > survey > no data) and frequency. Score on this indicator has a max of 1 point with 0.5 points for source quality and 0.5 points for frequency. Detailed scoring for source quality and frequency components are below.

Quality (0.5 points total): 0.5 Point. Vital Registration data available within past 5 years
0.25 Points. Survey Data availabe, but no Vital Registration data within past 5 years;
0 Points. None within past 5 years

Frequency (0.5 points total): 0.5 Point. 3 or more values available within past 5 years
0.3 Points. 2 values available within past 5 years;
0.15 Points. 1 values available within past 5 years;
0 Points. None within past 5 years

AKI 3.5 Maternal Mortality Ratio

The data is sourced from the the Inter-Agency Group (200 countries) to improve time and country coverages. Tne national estimates with the source information are available at https://www.who.int/reproductivehealth/publications/maternal-mortality-2000-2017/en/. The data was accessed on April 3, 2020. This data was used for modelling maternal mortality, which are then fed into the modelled estimates on Maternal Mortality (SH.STA.MMRT) in the WDI.

For our purposes, we will use the raw data from the country surveys as our data for the availability of key indicators, since the modelled estimates are produced out of sample, and thus not reflective of a country’s national statistical system.

Countries are ranked by source quality (admin data > survey > no data) and frequency. Score on this indicator has a max of 1 point with 0.5 points for source quality and 0.5 points for frequency. Detailed scoring for source quality and frequency components are below.

Quality (0.5 points total): 0.5 Point. Vital Registration data available within past 5 years
0.25 Points. Survey Data or Census Data availabe, but no Vital Registration data within past 5 years;
0 Points. None within past 5 years

Frequency (0.5 points total): 0.5 Point. 3 or more values available within past 5 years
0.3 Points. 2 values available within past 5 years;
0.15 Points. 1 values available within past 5 years;
0 Points. None within past 5 years

AKI 3.7: Access to electricity (% of population)

Access to Electricity (% of population) has 99% coverage in the WDI database making it less useful for distinguishing between countries. In order to improve the usefulness of this indicator, we are making use of data compiled by World Bank colleagues on surveys containing electricity items. According to the access to electricity metadata, around 42 countries have no data and an imputed value is assigned based on the regional average. By going to survey source data, we address this issue of imputation, which gives a misleading picture of availability for our purposes. Below is the metadata from the WDI for methods for the access to electricity indicator.

Data for access to electricity are collected among different sources: mostly data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Given the low frequency and the regional distribution of some surveys, a number of countries have gaps in available data. To develop the historical evolution and starting point of electrification rates, a simple modeling approach was adopted to fill in the missing data points - around 1990, around 2000, and around 2010. Therefore, a country can have a continuum of zero to three data points. There are 42 countries with zero data point and the weighted regional average was used as an estimate for electrification in each of the data periods. 170 countries have between one and three data points and missing data are estimated by using a model with region, country, and time variables. The model keeps the original observation if data is available for any of the time periods. This modeling approach allowed the estimation of electrification rates for 212 countries over these three time periods (Indicated as “Estimate”). Notation “Assumption” refers to the assumption of universal access in countries classified as developed by the United Nations. Data begins from the year in which the first survey data is available for each country.

Scoring 1 Point. 3 or more values available within past 5 years 0.6 Points. 2 values available within past 5 years;
0.3 Points. 1 values available within past 5 years;
0 Points. None within past 5 years

AKI 3.14 Quarterly GDP

Quarterly GDP numbers were pulled from the IMF website.

After GDP by expenditure, quarterly GDP is probably the most important development to be made in a system of National Accounts, before the development of full sectoral accounts. IMF IFS has quarterly GDP from various sources, including governments and international agencies at https://data.imf.org/?sk=4C514D48-B6BA-49ED-8AB9-52B0C1A0179B

Scoring 1 Point. 3 or more values available within past 5 years 0.6 Points. 2 values available within past 5 years;
0.3 Points. 1 values available within past 5 years;
0 Points. None within past 5 years

AKI 3.15 Debt service (PPG and IMF only, % of exports of goods, services and primary income)

For this indicator, Debt service (PPG and IMF only, % of exports of goods, services and primary income), we will pull data from the WDI but modify the scoring using the WDI metadata on whether the external debt data is actual, estimated, or preliminary. The status “as reported (actual)” indicates that the country was fully current in its reporting under the DRS and that World Bank staff are satisfied that the reported data give an adequate and fair representation of the country’s total public debt. “Preliminary” data are based on reported or collected information, but because of incompleteness or other reasons, an element of staff estimation is included. “Estimated” data indicate that countries are not current in their reporting and that a significant element of staff estimation has been necessary for producing the data tables.

Scoring is as follows:

Frequency:

0.5 Point. 3 or more values available within past 5 years
0.3 Points. 2 values available within past 5 years;
0.15 Points. 1 values available within past 5 years;
0 Points. None within past 5 years

Quality:

0.5 Points. Actual value 0.3 Points. Preliminary value 0.15 Points. Estimated value 0 Points. No value

## [1] "SPI.D3.POV"

## [1] "SPI.D3.FIES"

## [1] "SPI.D3.CHLD.MORT"

## [1] "SPI.D3.SE.LPV.PRIM.BMP"

## [1] "SPI.D3.MMRT"

## [1] "SPI.D3.SH.H2O.SMDW.ZS"

## [1] "SPI.D3.ELEC"

## [1] "SPI.D3.SL.UEM.TOTL.NE.ZS"

## [1] "SPI.D3.NV.IND.MANF.ZS"

## [1] "SPI.D3.SI.SPR.PC40.ZG"

## [1] "SPI.D3.ER.H2O.FWST.ZS"

## [1] "SPI.D3.EG.FEC.RNEW.ZS"

## [1] "SPI.D3.NE.CON.PRVT.CN"

## [1] "SPI.D3.QUART.GDP"

## [1] "SPI.D3.DT.TDS.DPPF.XP.ZS"

Data Sources

Cleaning for the Data Sources Indicators. Data Sources (4 Indicators):
- 4.1_SOCS - Indicator 4.1: censuses and surveys
- 4.2_SOAD - Indicator 4.2: administrative data
- 4.3_SOGS - Indicator 4.3: geospatial data
- 4.4_SOPC - Indicator 4.4: private/citizen generated data

Indicator 4.1: censuses and surveys

This indicator draws from data collected by the Statistical Performance Indicators team. The following censuses and surveys are considered:

• Population & Housing census
  
• Agriculture census
  
• Business/establishment census
  
• Household Survey on income/ consumption/ expenditure/ budget/ Integrated Survey
  
• Agriculture survey
  
• Labor Force Survey
  
• Health/Demographic survey
  
• Business/establishment survey

Indicator 4.2: administrative data

The following indicator checks whether administrative data is available for the following topic areas: Social Protection, Education, Population & Health, and Labor

Social Protection Admin Data

• Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE) is compilation of Social Protection and Labor (SPL) indicators to analyze scope and performance of SPL programs.
  
• ASPIRE has indicators for 136 countries on:
  • Social assistance
    
  • Social insurance
    
  • Labor market programs
• Based on both program-level administrative data and national household survey data
  
• Data extends from 2001 to 2018

Education Admin Data

• UNESCO UIS has shared a database containing data we can use on administrative data systems
  

• Two areas:

  • Data tracking SDG 4.1.2 Administration of a nationally-representative learning assessment (a) in Grade 2 or 3; (b) at the end of primary education; and (c) at the end of lower secondary education

  • Data tracking whether countries produce the following from administrative data sources:

    • Out-of-school rate by school age and sex
      
    • 4.2.2 Participation rate in organized learning (one year before the official primary entry age), by sex

Population & Health Admin Data

• This indicator is formed using World Bank metadata on whether the Civil Registration and Vital Statistics (CRVS) system is complete in the country.

Civil registration is the act of recording and documenting of vital events in a person’s life (including birth, marriage, divorce, adoption, and death and cause of death) and is a fundamental function of national governments. Birth registration establishes an individual’s legal identity at birth. A legal identity, name, nationality, and proof of age, are important human rights. They enable individuals to be included in various government, social and private services, and include the right to vote, etc. Vital statistics are compiled using civil registration information on these vital events. The availability of reliable and up-to-date vital statistics depends on the level of development of civil registration programs. An effective civil registration and vital statistics (CRVS) system is critical for planning and monitoring programs across several sectors.

By complete, that is representing 90 per cent or more of the events occurring in the specified year.

Source: World Bank WDI Metadata.

Labor Admin Data

• ILO shared database containing all administrative data sources used to produce ILO statistics (ILOSTAT)
  • May not be comprehensive of all administrative datasets available in a country
    
  • But gives some indication of whether labor statistics produced using Admin data

• Database contains the following admin sources:

  • Insurance records
    
  • Labour inspectorate records
    
  • Records of employers’ organizations
    
  • Employment office records
  • Records of workers’ organizations
  • Other administrative records and related sources

• Database extends from 2010 to 2019 and covers 177 countries

Indicator 4.3: geospatial data

New indicator based on references to geospatial data in metadata relating to content on NSO website

Source

Our source for this indicator is Open Data Watch. From Open Data watch:

The Open Data Inventory (ODIN) assesses the coverage and openness of official statistics to help identify gaps, promote open data policies, improve access, and encourage dialogue between national statistical offices (NSOs) and data users. ODIN 2018/19 includes 178 countries, including most all OECD countries. Two-year comparisons are for all countries with two years of data between 2015-2017. Scores can be compared across topics and countries.

We use their indicator on whether indicators are available at the first or second administrative level. To identify the first administrative levels, ODIN largely draws on the ISO 3166-2 standard. In many countries, first administrative levels refer to governorates, regions, or province. No official list exists for the second administrative level classifications. If geographical disaggregation exists that does not qualify as first administrative level, assume that the data are disaggregated to the second administrative level as long as the classification appears to be a further divisions of the first administrative level.

Scoring for the ODIN indicators for geospatial information is below:

• 1 point if all published data in a data category are available at first/second administrative level.
  
• 0.5 points if some published data in a data category are available at first/second administrative level.
  
• 0 points if no data are available at this level

There are 21 data categories.

Social Statistics
1. Population and Vital Statistics
2. Education Facilities
3. Education Outcomes
4. Health Facilities
5. Health Outcomes
6. Reproductive Health
7. Gender Statistics
8. Crime and Justice Statistics
9. Poverty Statistics

Economic Statistics
10. National Accounts
11. Labor Statistics
12. Price Indexes
13. Government Finance
14. Money and Banking
15. International Trade
16. Balance of Payments

Environmental Statistics
17. Land Use
18. Resource Use
19. Energy Use
20. Pollution
21. Built Environment

For the first administrative unit: Money & Banking, International Trade, and Balance of Payments are not scored for this element. For various indicators, lenient interpretations are used for first administrative divisions.

For the second administrative unit: Money & Banking, International Trade, Balance of Payments, National Accounts, Government Finance ,Pollution, Energy Use, Price Indexes, and Resource Use are not scored for this element. For various indicators within categories, second administrative level data is not required as well.

In the scores we present below, we show a score between 0 and 1 with a maximum score of 1, which would mean the country has geo data in full for 100% of elements. A score of 0 indicates no data at all for any elements.

More details on the geographic disaggregation considerations can be found in their technical manual:

https://docs.google.com/document/d/1ubPL1l_3im9bjlCVZ6W2ICAy6UAiXl1hGeA1aXImkxI/edit

Indicator 4.4: private/citizen generated data

New indicator based on references to private/citizen generated data in metadata relating to content on NSO website.

Covid Mobility Data Availability

We have seen an increased use of private/citizen generated data during the COVID-19 pandemic. One way in which it has been used is for tracking mobility of citizens to understand the social distancing measures citizens are taking. As a measure of private/citizen generated data we make use of Apple and Google Mobility tracking data made publicly available during the pandemic.

Source:

Google LLC “Google COVID-19 Community Mobility Reports”. https://www.google.com/covid19/mobility/ Accessed: 2020-07-23.

Apple “Mobility Trends Reports”. https://www.apple.com/covid19/mobility/ Accessed: 2020-07-23.

Data Infrastructure

Data Infrastructure (5 Indicators):
- 5.1_DILG - Indicator 5.1: legislation and governance
- 5.2_DISM - Indicator 5.2: standards
- 5.3_DISK - Indicator 5.3: skills
- 5.4_DIPN - Indicator 5.4: partnerships
- 5.5_DIFI - Indicator 5.5: finance

Indicator 5.1: legislation and governance

New indicator based on PARIS21 indicators on SDG 17.18.2 (national statistical legislation compliance with UN Fundamental Principles of Official Statistics), existence of National Statistical Council, national statistical strategy generation, national statistical plan. Also include some other legislative aspects that foster good use of statistics eg freedom of information, privacy/transparency, good governance (eg free and fair elections).

This indicator measures whether the national statistical legislation complies with United Nations Fundamental Principles of Statistics (SDG 17.18.2)

Scores is 1 if the country has a national statistical legislation compliant with United Nations Fundamental Principles of Statistics.

The source is Paris 21. Data accessed on August 6, 2020 from https://statisticalcapacitymonitor.org/indicator/135

## Indicator 5.2: standards

5.2.1 System of National Accounts in use

The national accounts data are compiled using the concepts, definitions, framework, and methodology of the System of National Account 2008 (SNA2008) or European System of National and Regional Accounts (ESA 2010). The manual has evolved to meet the changing economic structure, to follow systematic accounting and ensure international compatibility.

Scoring: 1 point for using SNA2008 or ESA 2010, 0.5 points for using SNA 1993 or ESA 1995, 0 points otherwise

5.2.2 National Accounts base year

National accounts base year is the year used as the base period for constant price calculations in the country’s national accounts. It is recommended that the base year of constant price estimates be changed periodically to reflect changes in economic structure and relative prices.

1 point for chained price, 0.5 for reference period within past 10 years, 0 points otherwise.

5.2.3 Classification of national industry

The industrial production data are compiled using the International Standard Industrial Classification of All Economic Activities (ISIC) Rev.4 and Statistical Classification of Economic Activities in the European Community (NACE) Rev.2. ISIC Rev.4 is a standard classification of economic activities arranged so that entities can be classified per the activity they carry out using criteria such as input, output and use of the products produced, more emphasis has been given to the character of the production process in defining and delineating ISIC classes for international comparability. The manual and classification have changed to cover the complete scope of industrial production, employment, and GDP and other statistical areas.

1 Point. Latest version is adopted (ISIC Rev 4, NACE Rev 2 or a compatible classification)

0.5 Points. Previous version is used (ISIC Rev 3, NACE Rev 1 or a compatible classification)

0 Points. Otherwise

5.2.4 CPI base year

Consumer Price Index serves as indicators of inflation and reflects changes in the cost of acquiring a fixed basket of goods and services by the average consumer.
Weights are usually derived from consumer expenditure surveys and the CPI base year refers to the year the weights were derived. It is recommended that the base year be changed periodically to reflect changes in expenditure structure.

1 Point. Annual chain linking. 0.5 Points. Base year in last 10 years. 0 points. Otherwise

5.2.5 Classification of household consumption

Classification of Individual Consumption According to Purpose (COICOP) is used in household budget surveys, consumer price indices and international comparisons of gross domestic product (GDP) and its component expenditures.
Although COICOP is not strictly linked to any particular model of consumer behavior, the classification is designed to broadly reflect differences in income elasticities. It is an integral part of the SNA1993 and more detailed subdivision of the classes provide comparability between countries and between statistics in these different areas.

1 Point. Follow Classification of Individual Consumption by Purpose (COICOP) 0 Points. Otherwise

5.2.6 Classification of status of employment

Classification of status of employment refers to employment data that are compiled using the current international standard International Classification of Status in Employment (ISCE-93). It classifies jobs with respect to the type of explicit or implicit contract of employment between the job holder and the economic unit in which he or she is employed. Therefore, it aims to provide the basis for production of internationally comparable statistics on the employment relationship, including the distinction between salaried employment and self-employment.

1 Point. Follow International Labour Organization, International Classification of Status in Employment (ICSE-93) or 2012 North American Industry Classification System (NAICS). 0 Points Otherwise.

5.2.7 Central government accounting status

Government finance accounting status refers to the accounting basis for reporting central government financial data. For many countries’ government finance data, have been consolidated into one set of accounts capturing all the central government’s fiscal activities and following noncash recording basis.
Budgetary central government accounts do not necessarily include all central government units, the picture they provide of central government activities is usually incomplete.

1 Point. Consolidated central government accounting follows noncash recording basis
0.5 Points. Consolidated central government accounting follows cash recording basis
0 Points. Otherwise

5.2.8 Compilation of government finance statistics

(GFSM) in use for compiling the data. It provides guidelines on the institutional structure of governments and the presentation of fiscal data in a format similar to business accounting with a balance sheet and income statement plus guidelines on the treatment of exchange rate and other valuation adjustments. The latest manual GFSM2014 is harmonized with the SNA2008.

1 Point. Follow the latest Government Finance Statistical Manual (2014)/ ESA2010
0.5 Points. Previous version is used (GFSM 2001)
0 Points. Otherwise

5.2.9 Compilation of monetary and financial statistics

Compilation of monetary and financial statistics refers to the Monetary and Financial Statistics Manual (MFSM) in use. It covers concepts, definitions, classifications of financial instruments and sectors, and accounting rules, and provides a comprehensive analytic framework for monetary and financial planning and policy determination. The Monetary and Finance Statistics: Compilation Guide (2008) provides detailed guidelines for the compilation of monetary and financial statistics in addition to MFSM.

1 Point. Follow the latest Monetary and Finance Statistics Manual (2000) or Monetary and Finance Statistics: Compilation Guide (2008/2016) 0 Points. Otherwise

5.2.10 Business process

The Generic Statistical Business Process Model (GSBPM) aims to describe statistics production in a general and process-oriented way. It is used both within and between statistical offices as a common basis for work with statistics production in different ways, such as quality, efficiency, standardization, and process-orientation. It is used for all types of surveys, and “business” is not related to “business statistics” but refers to the statistical office, simply expressed.

1 Point. GSBPM is in use 0 Points. Otherwise

Create Overall Indicator

Indicator 5.3: skills

This indicator assesses the systematic use of statistical knowledge with statistical terms and indicators in national policy documents. It is a composite indicator consisting of 4 sub-indices that aim to reflect the relevance of statistical evidence. It comprises four main dimensions: i) Basic consideration; ii) Diagnosis and quantification; iii) Statistical Analysis; iv) Disaggregation.

Score ranges from 0 to 100. The score is the weighted sum of each of the four components’ scores, whose relative weight is reported in brackets: i) Basic Consideration (25%); ii) Diagnosis and quantification (24%); iii) Statistical Analysis (22%); iv) Disaggregation (29%).

The source is Paris 21. Data accessed on August 6, 2020 from https://statisticalcapacitymonitor.org/indicator/127

Indicator 5.4: partnerships

No data is available for this indicator at this point.

Indicator 5.5: finance

Indicator based on PARIS21 SDG indicators (SDG 17.18.3 (national statistical plan that is fully funded and under implementation) and SDG 17.19.1 (value of resources made available to strengthen statistical capacity)). Could also incorporate indicator of NSO budget as a percentage of GDP.

Source of this data is Paris21. Downloaded on August 6,2020 from https://statisticalcapacitymonitor.org/indicator/138/